Name: Machine Learning and Data Science with Python: A Complete Beginners Guide
Price: 93.99 GBP
Availability: InStock

<strong>In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.</strong>
        <p>A complete guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models, and implement a suite of top machine learning algorithms using step-by-step tutorials.

Machine learning algorithms do have a lot of math and theory under the covers, but you do not need to know why algorithms work to be able to implement them and apply them to achieve real and valuable results.

In this course, you will learn how to load from CSV files and prepare data for modeling; how to select algorithm evaluation metrics and resampling techniques for a test harness; how to develop a baseline expectation of performance for a given problem; how to implement and apply a suite of linear machine learning algorithms; how to implement and apply a suite of advanced nonlinear machine learning algorithms; how to implement and apply ensemble machine learning algorithms to improve performance.

This course will be an invaluable guide to understanding real-world machine learning models and help you understand the code behind math.

By the end of this course, you will gain insight into real-world machine learning models and learn how to code the functions of the most used tools in machine learning.

The complete code bundle for this course is available at https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch</p>
        <h2>What You Will Learn</h2><p>Develop a baseline expectation of performance for a given problem<br>Learn to code the functions of the most used tools in machine learning<br>Gain insight into who real-world machine learning models are written<br>Gain a deep appreciation for how the algorithm works<br>Implement and apply a suite of linear machine learning algorithms<br>Implement and apply a suite of advanced non-linear ML algorithms</p>
        <h2>Audience</h2><p>This course is for developers, machine learning engineers, and data scientists who want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Additionally, you should have a solid background in Python.</p>
        <h2>Approach</h2><p>This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to the mechanics of machine learning models in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie, you will get little out of it.</p>
        <h2>Key Features</h2><p>Know how top machine learning algorithms work internally * Learn to configure machine learning algorithms to get the most out of them * Understand the myriad of micro-decisions that a machine learning library has hidden from you in practice</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch</p>
        <h2>About the Author</h2><h3>Mike West</h3><p>Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman.

Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Data Preparation</h4><p></p><br><h4>3. Linear Algorithms</h4><p></p><br><h4>4. Non-Linear Regression</h4><p></p>

Packt

Data Science

Python

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

Authoring Machine Learning Models from Scratch

General Admission

<strong>Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis</strong>
        <p>In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You'll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you'll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You'll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You'll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you'll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you'll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.
By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.

All code and supporting files for this course are available at 
https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.</p>
        <h2>What You Will Learn</h2><p>Harness the power of Anaconda/iPython for practical data science<br>Read data into the Python environment from different sources<br>Carry out basic data preprocessing and wrangling in Python<br>Implement unsupervised/clustering techniques such as k-means clustering<br>Get to grips with dimensionality reduction techniques and feature selection<br>Implement supervised learning/classification techniques such as random forests<br>Explore neural network- and deep learning-based classification<br></p>
        <h2>Audience</h2><p>If you want to build data science applications in the Python environment, this book is for you. You'll also find this book helpful if you want to learn how to implement unsupervised learning on real data using Python, or if you're a student looking to get started with artificial neural networks and deep learning.</p>
        <h2>Approach</h2><p>This course is full of interesting and illustrative examples and easy-to-understand theory to help you implement a real-world concept in every lecture. It covers hands-on methods to simplify and address even the most difficult concepts in Python using minimal jargon.</p>
        <h2>Key Features</h2><p>Explore the most important Python data science concepts and packages, including Pandas * Master the Anaconda framework and use it to implement clustering and classification models on your data * Get to grips with data science fundamentals and understand which models should be used when * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in Python</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Welcome to Clustering & Classification with Machine Learning in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning?</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: What is Machine Learning?</p></td></tr><tr><td style='padding: 15px;'><p>3. Python Data Science Environment</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Environment</p></td></tr><tr><td style='padding: 15px;'><p>4. For Mac Users</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: For Mac Users</p></td></tr><tr><td style='padding: 15px;'><p>5. IPython in Browser</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: IPython in Browser</p></td></tr><tr><td style='padding: 15px;'><p>6. Python Data Science Packages to Be Used</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Packages to Be Used</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources with Pandas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are Pandas?</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: What are Pandas?</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Data from CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from CSV</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Excel Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in HTML Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in HTML Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Databases</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from Databases</p></td></tr></tbody></table><br><h4>3. Data Cleaning & Munging</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Cleaning & Munging: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. Conditional Data Selection</p><p style='margin: 0'>Data Cleaning & Munging: Conditional Data Selection</p></td></tr><tr><td style='padding: 15px;'><p>3. Data Grouping</p><p style='margin: 0'>Data Cleaning & Munging: Data Grouping</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Sub setting</p><p style='margin: 0'>Data Cleaning & Munging: Data sub setting</p></td></tr><tr><td style='padding: 15px;'><p>5. Ranking & Sorting</p><p style='margin: 0'>Data Cleaning & Munging: Ranking & Sorting</p></td></tr><tr><td style='padding: 15px;'><p>6. Concatenate</p><p style='margin: 0'>Data Cleaning & Munging: Concatenate</p></td></tr><tr><td style='padding: 15px;'><p>7. Merging & Joining Data Frames</p><p style='margin: 0'>Data Cleaning & Munging: Merging & Joining Data Frames</p></td></tr></tbody></table><br><h4>4. Unsupervised Learning in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Unsupervised Classification- Some Basic Concepts</p><p style='margin: 0'>Unsupervised Learning in Python: Unsupervised Classification- Some Basic Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. K-Means Clustering: Theory</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering: Theory</p></td></tr><tr><td style='padding: 15px;'><p>3. Implement K-Means on the Iris Data</p><p style='margin: 0'>Unsupervised Learning in Python: Implement K-Means on the Iris Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Quantifying K-Means Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in Python: Quantifying K-Means Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>5. K-Means Clustering with Real Data</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering with Real Data</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Select the Optimal Number of Clusters?</p><p style='margin: 0'>Unsupervised Learning in Python: How to Select the Optimal Number of Clusters?</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Mixture Modelling (GMM)</p><p style='margin: 0'>Unsupervised Learning in Python: Gaussian Mixture Modelling (GMM)</p></td></tr><tr><td style='padding: 15px;'><p>8. Hierarchical Clustering-theory</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Hierarchical Clustering-practical</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-practical</p></td></tr></tbody></table><br><h4>5. Dimension Reduction & Feature Selection for Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Principal Component Analysis (PCA)-Theory</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)-Case Study 1</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA)-Case Study 2</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 2</p></td></tr><tr><td style='padding: 15px;'><p>4. Linear Discriminant Analysis (LDA) for Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Linear Discriminant Analysis (LDA) for Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>5. t-SNE Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: t-SNE Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>6. Feature Selection to Select the Most Relevant Predictors</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Feature Selection to Select the Most Relevant Predictors</p></td></tr><tr><td style='padding: 15px;'><p>7. Recursive Feature Elimination (RFE)</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Recursive Feature Elimination (RFE)</p></td></tr></tbody></table><br><h4>6. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Concepts Behind Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Concepts Behind Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preparation for Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Data Preparation for Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Pointers on Evaluating the Accuracy of Classification Modelling</p><p style='margin: 0'>Supervised Learning: Classification: Pointers on Evaluating the Accuracy of Classification Modelling</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Logistic Regression as a Classification Model</p><p style='margin: 0'>Supervised Learning: Classification: Using Logistic Regression as a Classification Model</p></td></tr><tr><td style='padding: 15px;'><p>5. kNN- Classification</p><p style='margin: 0'>Supervised Learning: Classification: kNN- Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. Naive Bayes Classification</p><p style='margin: 0'>Supervised Learning: Classification: Naive Bayes Classification</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. SVM- Linear Classification</p><p style='margin: 0'>Supervised Learning: Classification: SVM- Linear Classification</p></td></tr><tr><td style='padding: 15px;'><p>9. Non-Linear SVM Classification</p><p style='margin: 0'>Supervised Learning: Classification: Non-Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>10. RF-Classification</p><p style='margin: 0'>Supervised Learning: Classification: RF-Classification</p></td></tr><tr><td style='padding: 15px;'><p>11. Gradient Boosting Machine (GBM)</p><p style='margin: 0'>Supervised Learning: Classification: Gradient Boosting Machine (GBM)</p></td></tr><tr><td style='padding: 15px;'><p>12. Voting Classifier</p><p style='margin: 0'>Supervised Learning: Classification: Voting Classifier</p></td></tr></tbody></table><br><h4>7. Neural Networks and Deep Learning Based Classification Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Perceptrons for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Perceptrons for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>2. Artificial Neural Networks (ANN) for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Artificial Neural Networks (ANN) for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-class Classification With MLP</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Multi-class Classification With MLP</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to H20</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Introduction to H20</p></td></tr><tr><td style='padding: 15px;'><p>5. Use H20 for Deep Learning Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Use H20 for Deep Learning Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. H20 Deep Learning for Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: H20 Deep Learning for Classification</p></td></tr></tbody></table>

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Clustering and Classification with Machine Learning in Python

<p>Embark on a captivating journey into the world of artificial intelligence with our course, '<strong>Machine Learning Basics</strong>.' This voyage begins with an immersive introduction, setting the stage for an exploration into the intricate and fascinating realm of machine learning. Envision yourself unlocking the mysteries of algorithms and data patterns, essential skills in today's technology-driven landscape. The course offers a comprehensive foray into the core principles of machine learning, starting from the very basics and gradually building to more complex concepts, making it an ideal path for beginners and enthusiasts alike.</p>
<p>As you delve deeper, each section unravels a vital component of machine learning. Grasp the essentials of regression analysis, understand the role of predictors, and navigate through the functionalities of Minitab, a key tool in data analysis. Journey through the structured world of regression trees and binary logistic regression, and master the art of classification trees. The course also emphasizes the importance of data cleaning and constructing robust data models, culminating in the achievement of learning success. This course is not just an educational experience; it's a gateway to the future of data science and AI.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Comprehend the basic principles and applications of machine learning.</li>
<li>Develop proficiency in regression analysis and predictor identification.</li>
<li>Gain practical skills in Minitab for data analysis.</li>
<li>Understand and apply regression and classification trees.</li>
<li>Acquire expertise in data cleaning and model creation.</li>
</ul>
<h3><b>Why choose this Machine Learning Basics<span> course</span>?</b></h3>
<ol>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b> and <b>CIQ </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
<li>Unlock career resources for CV improvement, interview readiness, and job success.</li>
</ol>
<h3><b>Who is this Machine Learning Basics<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Novices eager to delve into machine learning.</li>
<li>Data enthusiasts looking to enhance their analytical skills.</li>
<li>Professionals in IT and related fields expanding their expertise.</li>
<li>Academics and students in computer science and data studies.</li>
<li>Career changers interested in the field of data science and AI.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Data Analyst</strong> - £30,000 to £55,000</li>
<li><strong>Machine Learning Engineer</strong> - £40,000 to £80,000</li>
<li><strong>AI Developer</strong> - £35,000 to £75,000</li>
<li><strong>Business Intelligence Analyst</strong> - £32,000 to £60,000</li>
<li><strong>Research Scientist (Machine Learning)</strong> - £45,000 to £85,000</li>
<li><strong>Software Engineer (AI Specialization)</strong> - £38,000 to £70,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Machine Learning Basics</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Machine Learning Basics</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Section 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Supervised Machine Learning</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Regression</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluating Regression Models</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditions for Using Regression Models in ML versus in Classical Statistics</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Statistically Significant Predictors</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Additive Effects</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Interaction Effects</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Predictors</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multicollinearity among Predictors and its Consequences</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Prediction for New Observation. Confidence Interval and Prediction Interval</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Model Building. What if the Regression Equation Contains 'Wrong' Predictors?</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Minitab</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Stepwise Regression and its Use for Finding the Optimal Model in Minitab</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: Regression Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Basic idea of Regression Trees</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part1</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Binary Logistics Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Binary Logistics Regression</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 07: Classification Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Classification Trees</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 1. Splitting by Misclassification Rate</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 2. Splitting by Gini Impurity or Entropy</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicted Class for a Node</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 1. Model Misclassification Cost</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predefined Prior Probabilities and Input Misclassification Costs</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Building the Tree</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Minitab. Example. Maintenance of Machines: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Miitab. Example. Maintenance of Machines: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: Data Cleaning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating New Features</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 09: Data Models</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polynomial Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Interactions Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Qualitative and Quantitative Predictors: Interaction Models</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Final Models for Duration and TotalCharge: Without Validation</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Underfitting or Overfitting: The 'Just Right Model'</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 10: Learning Success</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees for Duration and TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: The Problem Statement</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Binary Logistic Regression Models</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Classification Tree Models</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Learning & Development

Machine Learning (ML)

Learning To Learn

Unsupervised Machine Learning

Trainers Course

Azure Machine Learning

Machine Learning Basics

<strong>A code-oriented interactive course that will help you build a solid foundation that is essential to excel in all areas of computer science, specifically data science and machine learning. We will apply all concepts through code and focus on the concepts that are more useful for data science, machine learning, and other areas of computer science.</strong>
        <p>The objective of this course is to give you a solid foundation needed to excel in all areas of computer science-specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the math without discussing the importance of applications. Applications are always given secondary importance.

In this course, we take a code-oriented approach. We apply all concepts through code. In fact, we skip over all the useless theory that isn't relevant to computer science. Instead, we focus on the concepts that are more useful for data science, machine learning, and other areas of computer science. For instance, many probability courses skip over Bayesian inference. We will get to this immensely important concept rather quickly and give it due attention as it is widely thought of as the future of analysis!

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

All the resources for this course are available at: https://github.com/PacktPublishing/Probability-Statistics---The-Foundations-of-Machine-Learning</p>
        <h2>What You Will Learn</h2><p>Learn all necessary concepts in stats and probability<br>Learn important concepts for data science and/or machine learning<br>Understand distributions and their importance<br>Learn about Entropy, which is the foundation of all machine learning<br>Introduction to Bayesian Inference<br>Learn to apply concepts through code</p>
        <h2>Audience</h2><p>This course is designed for beginner ML and data science developers who need a solid foundation, for developers curious about data science and machine learning, for people looking to find out why probability is the foundation of all modern machine learning, or for developers who want to know how to harness the power of big data.</p>
        <h2>Approach</h2><p>In this course, we take a code-oriented approach. We will apply all concepts through code that are more useful for data science, machine learning, and other areas of computer science.</p>
        <h2>Key Features</h2><p>A practical approach towards understanding the core concepts of probability and statistics * Focuses on the applications of these important mathematical concepts in data science, machine learning, and other areas * Understand why probability is the foundation of all modern machine learning</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Probability-Statistics---The-Foundations-of-Machine-Learning</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Diving in with Code</h4><p></p><br><h4>2. Measures of Spread</h4><p></p><br><h4>3. Applications and Rules for Probability</h4><p></p><br><h4>4. Counting</h4><p></p><br><h4>5. Random Variables - Rationale and Applications</h4><p></p><br><h4>6. Visualization in Intuition Building</h4><p></p><br><h4>7. Applications to the Real World</h4><p></p>

A code-oriented interactive course that will help you build a solid foundation that is essential to excel in all areas of computer science, specifically data science and machine learning. We will apply all concepts through code and focus on the concepts that are more useful for data science, machine learning, and other areas of computer science.

Probability / Statistics - The Foundations of Machine Learning

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					<div><p>Learn the smart way of using machine learning in Flutter to build smart android and iOS applications with the Machine Learning in Flutter: Complete Guide course.</p><p>Flutter is a software development kit for developing beautiful applications from a single codebase. Moreover, if you can infuse machine learning within Flutter, you can craft wonders. This complete guide aims to provide you with the fundamental knowledge and skills required to use Machine Learning in Flutter to equip you with this phenomenal skill.</p><p>This complete guide will give you a solid foundation of the Firebase ML kit as well as a detailed idea about the image picker library and camera package setup for Flutter. You will explore a broad range of flutter widgets like image labelling, face detection, pose estimation and so on. </p><p>Enrol in this Machine Learning in Flutter: Complete Guide course today and build high-quality and functionally-rich apps using machine Learning in Flutter.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>Familiar yourself with the recent development and updates of the relevant industry</span>
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										<span>Know how to use your theoretical knowledge to adapt in any working environment</span>
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										<span>Get help from our expert tutors anytime you need</span>
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										<span>Access to course contents that are designed and prepared by industry professionals</span>
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										<span>Study at your convenient time and from wherever you want</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
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										<span>You will receive a completion certificate upon completing the course.</span>
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										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
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										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
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										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
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                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong> Unit 01: Introduction</strong></td>
				</tr>
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					<td>▶</td>
					<td>Module 01: Course Curriculum</td><td><span>🕐 00:02:00</span></td>
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					<td colspan="4"><strong>Unit 02: Image Picker and Camera Libraries</strong></td>
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					<td>▶</td>
					<td>Module 01: Image Picker Library for Flutter App Development</td><td><span>🕐 00:13:00</span></td>
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					<td>▶</td>
					<td>Module 02: Flutter Image Picker Application Testing</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 03: Camera Package Setup for Flutter</td><td><span>🕐 00:04:00</span></td>
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					<td>▶</td>
					<td>Module 04: Flutter Camera Package Code</td><td><span>🕐 00:08:00</span></td>
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					<td colspan="4"><strong>Unit 03: Firebase ML Kit</strong></td>
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					<td>▶</td>
					<td>Module 01: Firebase ML kit section Introduction</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 02: Firebase ML Kit introduction</td><td><span>🕐 00:02:00</span></td>
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					<td colspan="4"><strong>Unit 04: Image Labeling using ML Kit</strong></td>
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					<td>▶</td>
					<td>Module 01: Flutter Image Labeling Section Introduction</td><td><span>🕐 00:02:00</span></td>
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					<td>▶</td>
					<td>Module 02: Importing Starter code for image labeling</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 03: Image labeling starter code explanation</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 04: Creating firebase project for image labeling</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 05: Adding Firebase ML Vision library in Flutter Application</td><td><span>🕐 00:10:00</span></td>
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					<td>Module 06: Testing Firebase Image labeling application</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 07: Importing Image Labeling live feed application starter code</td><td><span>🕐 00:03:00</span></td>
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					<td>Module 08: Flutter Camera Package Code</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 09: Flutter Image Labeling live feed application code</td><td><span>🕐 00:08:00</span></td>
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					<td>Module 10: Flutter Image labeling live feed application testing</td><td><span>🕐 00:01:00</span></td>
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					<td colspan="4"><strong>Unit 05: Section Barcode Scanning</strong></td>
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					<td>Module 01: Flutter Barcode Scanning Section Introduction</td><td><span>🕐 00:02:00</span></td>
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					<td>Module 02: Importing Starter code for Flutter Barcode Scanning</td><td><span>🕐 00:03:00</span></td>
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					<td>Module 03: Flutter Barcode Scanning code</td><td><span>🕐 00:11:00</span></td>
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					<td>▶</td>
					<td>Module 04: Flutter Barcode Scanning Application Testing</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 05: Flutter Barcode Scanning Live Feed Application code</td><td><span>🕐 00:08:00</span></td>
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					<td>▶</td>
					<td>Module 06: Flutter Barcode Scanning Live feed Application Testing</td><td><span>🕐 00:01:00</span></td>
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					<td colspan="4"><strong>Unit 06: Section Text Recognition</strong></td>
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					<td>Module 01: Flutter Text Recognition Section Introduction</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 02: Importing Starter code for Flutter Text Recognition</td><td><span>🕐 00:03:00</span></td>
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					<td>Module 03: Writing Flutter Text Recognition Code</td><td><span>🕐 00:09:00</span></td>
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					<td>▶</td>
					<td>Module 04: Testing Flutter Text Recognition Application</td><td><span>🕐 00:01:00</span></td>
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					<td colspan="4"><strong>Unit 07: Section Face Detection</strong></td>
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					<td>▶</td>
					<td>Module 01: Flutter Face Detection Section Introduction</td><td><span>🕐 00:02:00</span></td>
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					<td>▶</td>
					<td>Module 02: Flutter Face Detection Application Flow</td><td><span>🕐 00:01:00</span></td>
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					<td>▶</td>
					<td>Module 03: Flutter Face Detection code</td><td><span>🕐 00:06:00</span></td>
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					<td>▶</td>
					<td>Module 04: Flutter drawing rectangles around detected faces</td><td><span>🕐 00:05:00</span></td>
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					<td colspan="4"><strong>Unit 08: Pretrained Tensorflow lite models</strong></td>
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					<td>▶</td>
					<td>Module 01: Pretrained Tensorflow lite models Section Introduction</td><td><span>🕐 00:02:00</span></td>
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					<td colspan="4"><strong>Unit 09: Section Image Classification</strong></td>
				</tr>
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					<td>▶</td>
					<td>Module 01: Flutter Image classification Section introduction</td><td><span>🕐 00:02:00</span></td>
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					<td>▶</td>
					<td>Module 02: Importing Starter code for Flutter Image classification application</td><td><span>🕐 00:03:00</span></td>
				</tr>
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					<td>▶</td>
					<td>Module 03: Starter code explanation for Flutter Image classification</td><td><span>🕐 00:06:00</span></td>
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					<td>▶</td>
					<td>Module 04: Writing flutter image classification code</td><td><span>🕐 00:13:00</span></td>
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								<tr>
					<td>▶</td>
					<td>Module 05: Testing flutter image classification application</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 06: Importing Flutter live feed Image classification application starter code</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 07: Starter code explanation of Flutter Live feed Image classification application</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 08: Writing Flutter Image classification code</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 09: Testing live feed image classification flutter application</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 10: Section object detection</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Flutter Object detection section introduction</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Importing Application code object detection flutter</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 03: Flutter Object detection code</td><td><span>🕐 00:13:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 04: Flutter Drawing Rectangles around detected objects</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 05: Importing the code for live feed object detection flutter application</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 06: Testing object detection live feed flutter application</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 07: Flutter Live feed object detection application code</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 11: Section human pose estimation</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Flutter Pose estimation section introduction</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Importing Flutter Pose estimation Application code</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 03: Flutter Pose estimation code</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 04: Importing pose estimation live feed flutter application code</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 05: Flutter Live feed pose estimation application demo</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 06: Using PoseNet model for Flutter Live feed pose estimation application</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 12: Image segmentation section</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Flutter Image Segmentation Section Introduction</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Importing Flutter Image Segmentation Application code</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 03: Flutter using DeepLab model for image segmentation</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 13: Section Training Image Classification Models</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Section Introduction</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Machine Learning and Image classification</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 14: Dog Breed Classification</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Flutter getting the dataset for model training</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Flutter Training the model</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 03: Flutter Dog Breed Classification Application</td><td><span>🕐 00:18:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 04: Flutter Live feed dog breed classification application</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 05: Testing live feed dog breed classification application</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Unit 15: Fruits Recognition using Transfer Learning</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 01: Transfer learning introduction</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 02: Flutter getting the dataset for model training</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 03: Flutter Training fruit recognition model</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Module 04: Flutter Testing Live feed fruits recognition application</td><td><span>🕐 00:01:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Skill Up

Learn the smart way of using machine learning in Flutter to build smart android and iOS applications with

Machine Learning in Flutter: Complete Guide

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

LearnDrive UK

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

Janets

Coordinate Measuring Machine (CMM)

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Data Science & Machine Learning With Python

<p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.&nbsp;</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You&#39;ll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Enrol now to gain instant access to the course materials and start your learning journey. Don&#39;t miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.</span></p><h2 dir="ltr" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Learning Outcome of Python for Data Science and Machine Learning Bootcamp &nbsp; &nbsp;</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Master fundamental concepts of machine learning and its applications.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Gain proficiency in Python programming for data analysis and manipulation.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to load, read, and manipulate CSV data files using Python libraries.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Understand dataset summary statistics and visualization techniques.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Acquire skills in data preparation and preprocessing.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Explore feature selection techniques and their importance.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Evaluate machine learning algorithms using various techniques and metrics.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Spot check classification and regression algorithms for model selection.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Improve model performance using ensemble methods and parameter tuning.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to export, save, load, and finalize machine learning models for real-time predictions.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Key Features of the Course</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A CPD certificate that is recognised worldwide.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A great online learning experience.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Interesting and unique online materials and activities.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Expert guidance and support from the field leaders.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Access to the study resources anytime you want.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Get a year-long access to the course.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Who is this course for</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); font-size: 16px; box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0); font-size: 16px;">This Python for Data Science and Machine Learning Bootcamp Course is suitable for -</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Aspiring data scientists and machine learning enthusiasts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Professionals seeking to enhance their data analysis skills</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Individuals interested in Python programming for data science</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Anyone looking to break into the field of data science and machine learning</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Students pursuing studies in computer science or related fields</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Requirements</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Basic understanding of programming concepts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Familiarity with Python programming language is beneficial but not mandatory</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">No prerequisites; suitable for individuals from any academic background.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Accessible course materials from any internet-enabled device.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">CPD Certificate from Course Gate</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for &pound;9 and the hard copy for &pound;15. Also, you can order both PDF and hardcopy certificates for &pound;22.</span></p><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Career path</span><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></span></h2><div style="color: rgb(68, 68, 68); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(68, 68, 68);"><div style="color: rgb(68, 68, 68); box-sizing: border-box;" fr-original-style=" color: rgb(68, 68, 68);"><div data-elementor-type="wp-post" data-elementor-id="357815" data-elementor-post-type="course" fr-original-style="" style="box-sizing: border-box;"><section data-id="5767f71a" data-element_type="section" fr-original-style="" style="box-sizing: border-box;"><div data-id="58784751" data-element_type="column" fr-original-style="" style="box-sizing: border-box;"><div data-id="ce105db" data-element_type="widget" data-widget_type="text-editor.default" style="color: var( --e-global-color-text ); box-sizing: border-box;" fr-original-style=" color: var( --e-global-color-text );"><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Machine Learning Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Business Intelligence Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Research Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Quantitative Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Artificial Intelligence Specialist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Consultant</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Statistician</li></ul></div></div></section></div></div></div>

Course Gate

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills. 

Python for Data Science and Machine Learning Bootcamp

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning</p>
        
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

<strong>This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.</strong>
        <p>Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They're also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science that tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.
You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.

Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide</p>
        <h2>What You Will Learn</h2><p>Install Python and required libraries<br>Choose the best machine learning model<br>Automate and combine workflows with pipeline<br>Look at performance improvement with ensembles<br>Study performance improvement with algorithm parameter tuning<br>Finalize a machine learning project</p>
        <h2>Audience</h2><p>This course is for beginners who are interested in machine learning using Python.</p>
        <h2>Approach</h2><p>An exhaustive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Learn machine learning and data science using Python * A practical course designed for beginners who are interested in machine learning using Python * Work on predictions and case studies</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-and-data-science-with-python-a-complete-beginners-guide</p>
        <h2>About the Author</h2><h3>Abhilash Nelson </h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than eight years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Overview and Table of Contents</h4><p></p><br><h4>2. Introduction to Machine Learning</h4><p></p><br><h4>3. System and Environment Preparation</h4><p></p><br><h4>4. Learn Basics of Python</h4><p></p><br><h4>5. Learn Basics of NumPy</h4><p></p><br><h4>6. Learn Basics of Matplotlib</h4><p></p><br><h4>7. Learn Basics of Pandas</h4><p></p><br><h4>8. Understanding the CSV Data File</h4><p></p><br><h4>9. Load and Read CSV Data File</h4><p></p><br><h4>10. Dataset Summary</h4><p></p><br><h4>11. Dataset Visualization</h4><p></p><br><h4>12. Data Preparation</h4><p></p><br><h4>13. Feature Selection</h4><p></p><br><h4>14. Refresher Session - the Mechanism of Re-Sampling, Training, and Testing</h4><p></p><br><h4>15. Algorithm Evaluation Techniques</h4><p></p><br><h4>16. Algorithm Evaluation Metrics</h4><p></p><br><h4>17. Classification Algorithm Spot Check - Logistic Regression</h4><p></p><br><h4>18. Classification Algorithm Spot Check - Linear Discriminant Analysis</h4><p></p><br><h4>19. Classification Algorithm Spot Check - K-Nearest Neighbors</h4><p></p><br><h4>20. Classification Algorithm Spot Check - Naive Bayes</h4><p></p><br><h4>21. Classification Algorithm Spot Check - CART</h4><p></p><br><h4>22. Classification Algorithm Spot Check - Support Vector Machines</h4><p></p><br><h4>23. Regression Algorithm Spot Check - Linear Regression</h4><p></p><br><h4>24. Regression Algorithm Spot Check - Ridge Regression</h4><p></p><br><h4>25. Regression Algorithm Spot Check - LASSO Linear Regression</h4><p></p><br><h4>26. Regression Algorithm Spot Check - Elastic Net Regression</h4><p></p><br><h4>27. Regression Algorithm Spot Check - K-Nearest Neighbors</h4><p></p><br><h4>28. Regression Algorithm Spot Check - CART</h4><p></p><br><h4>29. Regression Algorithm Spot Check - Support Vector Machines (SVM)</h4><p></p><br><h4>30. Compare Algorithms - Part 1: Choosing the Best Machine Learning Model</h4><p></p><br><h4>31. Compare Algorithms - Part 2: Choosing the Best Machine Learning Model</h4><p></p><br><h4>32. Pipelines: Data Preparation and Data Modelling</h4><p></p><br><h4>33. Pipelines: Feature Selection and Data Modelling</h4><p></p><br><h4>34. Performance Improvement: Ensembles - Voting</h4><p></p><br><h4>35. Performance Improvement: Ensembles - Bagging</h4><p></p><br><h4>36. Performance Improvement: Ensembles - Boosting</h4><p></p><br><h4>37. Performance Improvement: Parameter Tuning Using Grid Search</h4><p></p><br><h4>38. Performance Improvement: Parameter Tuning Using Random Search</h4><p></p><br><h4>39. Export, Save and Load Machine Learning Models: Pickle</h4><p></p><br><h4>40. Export, Save and Load Machine Learning Models: Joblib</h4><p></p><br><h4>41. Finalizing a Model - Introduction and Steps</h4><p></p><br><h4>42. Finalizing a Classification Model - the Pima Indian Diabetes Dataset</h4><p></p><br><h4>43. Quick Session: Imbalanced Dataset - Issue Overview and Steps</h4><p></p><br><h4>44. Iris Dataset: Finalizing Multi-Class Dataset</h4><p></p><br><h4>45. Finalizing a Regression Model - the Boston Housing Price Dataset</h4><p></p><br><h4>46. Real-Time Predictions: Using the Pima Indian Diabetes Classification Model</h4><p></p><br><h4>47. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset</h4><p></p><br><h4>48. Real-Time Predictions: Using the Boston Housing Regression Model</h4><p></p>

This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.

Machine Learning and Data Science with Python: A Complete Beginners Guide

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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